The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
The Art and Practice of Systems Biology in Medicine: Mapping Patterns of Relationships
Abstract
Systems biology has developed in recent years from a technology-driven enterprise to a new strategic tool in Life Sciences, particularly for innovative drug discovery and drug development. Combining the ultimate in systems phenotyping with in-depth investigations of biomolecular mechanisms will enable a revolution in our understanding of disease pathology and will advance translational medicine, combination therapies, integrative medicine, and personalized medicine. A prerequisite for deriving the benefits of such a systems approach is a reliable and well-validated bioanalytical platform across complementary measurement modalities, especially transcriptomics, proteomics, and metabolomics, that operates in concert with a megavariate integrative biostatistical/bioinformatics platform. The applicable bioanalytical methodologies must undergo an intense development trajectory to reach an optimal level of reliable performance and quantitative reproducibility in daily practice. Moreover, to generate such enabling systems information, it is essential to design experiments based on an understanding of the complexity and statistical characteristics of the large data sets created. Novel insights into biology and system science can be obtained by evaluating the molecular connectivity within a system through correlation networks, by monitoring the dynamics of a system, or by measuring the system responses to perturbations such as drug administration or challenge tests. In addition, cross-compartment communication and control/feed-back mechanisms can be studied via correlation network analyses. All these data analyses depend critically upon the generation of high-quality bioanalytical platform data sets. The emphasis of this paper is on the characteristics of a bioanalytical platform that we have developed to generate such data sets. The broad applicability of Systems Biology in pharmaceutical research and development is discussed with examples in disease biomarker research, in pharmacology using system response monitoring, and in cross-compartment system toxicology assessment.
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Cites
Ross, P. L., Huang, Y. N., Marchese, J. N., Williamson, B., Parker, K., Hattan, S., Khainovski, N., Pillai, S., Dey, S., Daniels, S., et al. (2004) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m400129-mcp200 · Cited by 4,401
Self-citationvan den Berg, R. A., Hoefsloot, H. C., Westerhuis, J. A., Smilde, A. K., & van der Werf, M. J. (2006) · BMC Genomics · doi:10.1186/1471-2164-7-142 · Cited by 2,450
Computational systems biology.
Kitano, H. (2002) · Nature · doi:10.1038/nature01254 · Cited by 2,209
A N EW A PPROACH TO D ECODING L IFE : Systems Biology
Ideker, T., Galitski, T., & Hood, L. (2001) · Annual Review of Genomics and Human Genetics · doi:10.1146/annurev.genom.2.1.343 · Cited by 1,664
Multicomponent therapeutics for networked systems
Keith, C. T., Borisy, A. A., & Stockwell, B. R. (2005) · Nature Reviews Drug Discovery · doi:10.1038/nrd1609 · Cited by 769
Cited by
The Human Serum Metabolome (HUSERMET) Consortium, Dunn, W. B., Broadhurst, D., Begley, P., Zelena, E., Francis-McIntyre, S., Anderson, N., Brown, M., Knowles, J. D., Halsall, A., et al. (2011) · Nature Protocols · doi:10.1038/nprot.2011.335 · Cited by 3,294
Scalbert, A., Brennan, L., Fiehn, O., Hankemeier, T., Kristal, B. S., van Ommen, B., Pujos-Guillot, E., Verheij, E., Wishart, D., & Wopereis, S. (2009) · Metabolomics · doi:10.1007/s11306-009-0168-0 · Cited by 565
Self-citationKell, D. B. (2009) · BMC Medical Genomics · doi:10.1186/1755-8794-2-2 · Cited by 554
Quantitative metabolomics based on gas chromatography mass spectrometry: status and perspectives
Koek, M. M., Jellema, R. H., van der Greef, J., Tas, A. C., & Hankemeier, T. (2011) · Metabolomics · doi:10.1007/s11306-010-0254-3 · Cited by 353
Self-citationvan der Kloet, F. M., Bobeldijk, I., Verheij, E. R., & Jellema, R. H. (2009) · Journal of Proteome Research · doi:10.1021/pr900499r · Cited by 317
Self-citationMetabolomics: A Global Biochemical Approach to the Study of Central Nervous System Diseases
Kaddurah-Daouk, R., & Krishnan, K. R. R. (2009) · Neuropsychopharmacology · doi:10.1038/npp.2008.174 · Cited by 293
Dunn, W. B. (2008) · Physical Biology · doi:10.1088/1478-3975/5/1/011001 · Cited by 278
Multilevel data analysis of a crossover designed human nutritional intervention study
van Velzen, E. J. J., Westerhuis, J. A., van Duynhoven, J. P. M., van Dorsten, F. A., Hoefsloot, H. C. J., Jacobs, D. M., Smit, S., Draijer, R., Kroner, C. I., & Smilde, A. K. (2008) · Journal of Proteome Research · doi:10.1021/pr800145j · Cited by 161
Wopereis, S., Rubingh, C. M., van Erk, M. J., Verheij, E. R., van Vliet, T., Cnubben, N. H. P., Smilde, A. K., van der Greef, J., van Ommen, B., & Hendriks, H. F. J. (2009) · PLoS ONE · doi:10.1371/journal.pone.0004525 · Cited by 123
Self-citationGet To The Point! Problem-Based Curated Data Views To Augment Care For Critically Ill Patients
Zhang, M., Ehrmann, D., Mazwi, M., Eytan, D., Ghassemi, M., & Chevalier, F. (2022) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3491102.3501887 · Cited by 16
Metadata
- Authors
- van der Greef, J., Martin, S., Juhasz, P., Adourian, A., Plasterer, T., Verheij, E. R., & McBurney, R. N.
- Venue
- Journal of Proteome Research
- Publisher
- American Chemical Society (ACS)
- Published
- 2007-03-21
- Volume
- 6
- Issue
- 4
- Pages
- 1540-1559
- ISSN
- 1535-3893 (ISSN-L)
- 1535-3907
- OpenAlex
- W2000009197
- Cited by
- 201
- References
- 5
- FWCI
- 9.82
- Type
- journal article
- Language
- en
- Open access
- closed
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